Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential

TL;DR

This paper presents a machine learning-based computational tool to predict the thermal half-lives of azobenzene derivatives, aiding the design of light-activated drugs by understanding their isomerization dynamics.

Contribution

It introduces an automated, accurate machine learning approach incorporating intersystem crossing to predict azobenzene derivatives' thermal half-lives, covering a large chemical space.

Findings

Predicted thermal half-lives for 19,000 derivatives.

Identified trends between barriers and absorption wavelengths.

Open-sourced data and software for further research.

Abstract

Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and tradeoffs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.